The conventional approach to grasping objects with a robotic arm has been to first assume an accurate model of the object to be grasped and with an off-line geometric algorithm to use that model to determine a set of grip points at which the gripping members, such as fingers, are to be placed. (See, for example, B. Mishra et al., "On the Existence and Synthesis of Multifinger Positive Grips", Algorithmica, 2:541-558, 1987). Once the grip points have been determined, the geometry of the object is deemed irrelevant and the grasp is determined and maintained by controlling the magnitudes of the forces at the grip points.
Such an approach, however, has not proven very useful in practice, having turned out to lack robustness. The only success in dexterous robotic manipulation of objects seems to have come from two directions: 1) telemanipulation, where a human in the loop uses much more sensory information than is assumed to be theoretically necessary (see D. Clark et al., "Teleoperating the Utah/MIT Hand with a VPL Dataglove", Tech. Report #169, New York University, September 1988), and 2) simple parallel jaw grippers, where grasping algorithms can be made immune to the lack of any sensory information. An example of an approach in the latter direction is to remove friction in the transversal direction between a jaw of the gripper and the grasped object. (See K. Y. Goldberg, "Stochastic Plans for Robotic Manipulation", Ph.D. Thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pa., 1990). In this approach, the basic gripper has been modified to reduce the friction between the gripper and the object being grasped. This friction occurs when the gripper is being closed on an object, and this object is forced to rotate.
There have been several studies on finding grasps for parallel jaw grippers when the object to be grasped is known. (See, e.g., A. S. Rao, et al., "Shape from Diameter: Strategies for Recognizing Polygonal Parts", Tech. Report #292, University of Southern California, Inst. of Robotics and Intelligent Systems (IRIS), Los Angeles, Calif., 1992; A. S. Rao, et al., "Grasping Curved Planar Parts with a Parallel Jaw Gripper", Technical Report #299, IRIS, August 1992). A more recent approach deals with grasping unknown objects by closing the gripper on an unknown object several times with the gripper at different orientations and measuring the jaw opening distance at each of the different orientations of the gripper. (See A. S. Rao, "Algorithmic Plans for Robotic Manipulation", PhD Thesis, University of Southern California, Los Angeles, May 1993). Although this scheme allows the determination of an unknown object, it also subjects the object to many motions.
Another method involves grasping an unknown object in random orientations to distinguish planar parts. In the computational geometry literature, there are several reported results on probing, such as by using finger probes (see, e.g., R. Cole et al., "Shape from Probing" J Algorithms, Vol 8, No 1, pages 11-38, 1987), or line and other probes (see, e.g., D. Dobkin et al., "Probing Convex Polytopes", Proceedings of the 18th